Temporal Scheme Research Articles

Low-latency full-field temporal magnification based on spectral compression

Temporal magnification is an emerging technology for the observation of single-shot optical signals with irregular and ultrafast dynamics, which exceed the speed, precision, and record length of conventional digitizers. Conventional temporal magnification schemes suffer from transmission delay and large volume of dispersive elements. Because only the signal envelope can be magnified in the dispersion-based schemes, real-time full-field (phase and amplitude) measurement for a complex ultrafast optical signal remains an open challenge. Here, a bandwidth-compressed temporal magnification scheme for low-latency full-field measurements of ultrafast dynamics is proposed. Unlike the dispersion-based schemes, temporal magnification of a complex optical signal is achieved by bandwidth compression. The bandwidth is coherently compressed by the Vernier effect relying on the detuned free spectral range of a periodic optical filter and time lens. Experimentally, a temporal magnification factor of 224 is realized, and full-field measurements for picosecond pulses are demonstrated. The proposal eliminates the dependence on dispersive elements and shows great potential in integration, which may pave a new path toward full-field measurement for nonrepetitive and statistically rare signals.

A Continuity Flow Based Tomographic Reconstruction Algorithm for 4D Multi-Beam High Temporal-Low Angular Sampling.

A mathematical framework and accompanying numerical algorithm exploiting the continuity equation for 4D reconstruction of spatiotemporal attenuation fields from multi-angle full-field transmission measurements is presented. The algorithm is geared towards rotation-free dynamic multi-beam X-ray tomography measurements, for which angular information is sparse but the temporal information is rich. 3D attenuation maps are recovered by propagating an initial discretized density volume in time according to the advection equations using the Finite Volumes method with a total variation diminishing monotonic upstream-centered scheme (TVDMUSCL). The benefits and limitations of the algorithm are explored using dynamic granular system phantoms modelled via discrete elements and projected by an analytical ray model independent from the numerical ray model used in the reconstruction scheme. Three phantom scenarios of increasing complexity are presented and it is found that projections from only a few (unknowns:equations > 10) angles can be sufficient for characterisation of the 3D attenuation field evolution in time. It is shown that the artificial velocity field produced by the algorithm sub-iteration, which is used to propagate the attenuation field, can to some extent approximate the true kinematics of the system. Furthermore, it is found that the selection of a temporal interpolation scheme for projection data can have a significant impact on error build up in the reconstructed attenuation field.

Performance of fast fluid dynamics with a semi-Lagrangian scheme and an implicit upwind scheme in simulating indoor/outdoor airflow

Computational fluid dynamics can be time consuming for predicting indoor airflows and pollutant transport in large-scale problems or emergency management. Fast fluid dynamics (FFD) is able to accomplish efficient and accurate simulation of indoor/outdoor airflow. FFD solves the advection term of the Navier–Stokes equations either by a semi-Lagrangian (SL) scheme or an implicit upwind (IU) scheme. The SL scheme can be highly efficient, but its first-order version is not conservative and introduces significant numerical diffusion. To improve its accuracy, a high-order temporal and interpolation scheme that not only reduces dissipation and dispersion errors but also guarantees the convergence speed should be applied. Otherwise, an IU scheme instead could be used to solve the advection term. The IU scheme is conservative and introduces minor numerical diffusion, but it may increase the computation time. Therefore, this study investigated the performance of FFD with SL scheme using high-order temporal and interpolation schemes and that with IU scheme. The comparisons used experimental data of two indoor airflows and one outdoor airflow. The results showed that FFD with IU scheme was overall more accurate than FFD with SL scheme. In simulating indoor airflow, both methods were robust and the predictions were independent of time step sizes if the Courant number was less than or equal to one. In simulating the outdoor airflow, the FFD with SL scheme performed better than the FFD with IU scheme for large time step sizes. The FFD with IU scheme consumed 44%–61% computing time of the FFD with SL scheme.

The Effect of Spatial Resolution and Temporal Sampling Schemes on the Measurement Error for a Moon-Based Earth Radiation Observatory

The Moon-Based Earth Radiation Observatory (MERO) is a new platform, which is expected to advance current Earth radiation budget (ERB) research with better observations. For the instrument design of a MERO system, ascertaining the spatial resolution and sampling scheme is important. However, current knowledge about this is still limited. Here we proposed a simulation method for the MERO-measured Earth top of atmosphere (TOA) outgoing shortwave radiation (OSR) and outgoing longwave radiation (OLR) fluxes and constructed the “true” Earth TOA OSR and OLR fluxes based on the Clouds and Earth’s Radiant Energy System (CERES) data. Then we used them to reveal the effects of spatial resolution and temporal scheme (sampling interval and the temporal sampling sequence) on the measurement error of a MERO. Our results indicate that the spatial sampling error in the unit of percentage reduces linearly as the spatial resolution varies from 1000 km to 100 km; the rate is 2.5%/100 km for the Earth TOA OSR flux, which is higher than that (1%/100 km) of the TOA OLR flux. Besides, this rate becomes larger when the spatial resolution is finer than 40 km. It is also demonstrated that a sampling temporal sequence of starting time of 64 min with a sampling interval of 90 min is the optimal sampling scheme that results in the least temporal sampling error for the MERO system with a 40 km spatial resolution, note that this conclusion depends on the temporal resolution and quality of the data used to construct the “true” Earth TOA OSR and OLR fluxes. The proposed method and derived results in this study could facilitate the ascertainment of the optimal spatial resolution and sampling scheme of a MERO system under certain manufacturing budget and measurement error limit.

Acoustic wave propagation with new spatial implicit and temporal high-order staggered-grid finite-difference schemes

AbstractThe implicit staggered-grid (SG) finite-difference (FD) method can obtain significant improvement in spatial accuracy for performing numerical simulations of wave equations. Normally, the second-order central grid FD formulas are used to approximate the temporal derivatives, and a relatively fine time step has to be used to reduce the temporal dispersion. To obtain high accuracy both in space and time, we propose a new spatial implicit and temporal high-order SG FD stencil in the time–space domain by incorporating some additional grid points to the conventional implicit FD one. Instead of attaining the implicit FD coefficients by approximating spatial derivatives only, we calculate the coefficients by approximating the temporal and spatial derivatives simultaneously through matching the dispersion formula of the seismic wave equation and compute the FD coefficients of our new stencil by two schemes. The first one is adopting a variable substitution-based Taylor-series expansion (TE) to derive the FD coefficients, which can attain (2M + 2)th-order spatial accuracy and (2N)th-order temporal accuracy. Note that the dispersion formula of our new stencil is non-linear with respect to the axial and off-axial FD coefficients, it is complicated to obtain the optimal spatial and temporal FD coefficients simultaneously. To tackle the issue, we further develop a linear optimisation strategy by minimising the L2-norm errors of the dispersion formula to further improve the accuracy. Dispersion analysis, stability analysis and modelling examples demonstrate the accuracy, stability and efficiency advantages of our two new schemes.

Machine Learning based Classification of Local Robotic Surgical Skills in a Training Tasks Set.

During surgical training, it is important for the surgeon develops good motor skills throughout his training. For this reason, various surgical training systems have been developed to enhance these skills. However, one of the great challenges in these training systems is being able to objectively measure the ability and performance of the main surgical tasks, where currently only a global measurement is obtained once the task is completed. In this work, a temporal evaluation scheme is proposed, that is, an evaluation of local surgical performance at different time intervals during the training of typical tasks (knot-tying, needle-passing and suturing). The goal is to automatically classify expert (experience >100 hrs) and non-expert (experience <10 hrs) surgeons according to their performance during training, based on three classifiers: K-Nearest Neighborhood, Random Forest, and Support Vector Machine Unlike other previously reported methods, this work proposes a new evaluation scheme based on segments or time intervals, which can be an indicator of the surgeon's local performance during a robotic surgical task, without the need for direct labeling of the data at the segment level. The classification performance from obtained results was in accuracy 83% to 100%, 88% to 100% of AUC-ROC, and 88% to 100% of F1-Score in the final test between experts and non-experts surgeons, where the Support Vector Machine classifier presented the best performance. These results suggest that this proposed method by time intervals could be used in various surgical trainers to evaluate the local performance of a surgeon during trainingand thus be able to provide a tool for the quantitative visualization of opportunities to improve surgical skills.Clinical relevance- We consider that the proposed method to carry out a local performance evaluation during surgical training can provide useful information in the learning and improvement of surgical skills.

Fast L2-1[formula omitted] Galerkin FEMs for generalized nonlinear coupled Schrödinger equations with Caputo derivatives

The paper is concerned with the unconditional stability and optimal error estimates of Galerkin finite element methods (FEMs) for a class of generalized nonlinear coupled Schrödinger equations with Caputo-type derivatives. We improve the results in [1] to a higher order temporal scheme by using a type of new Grönwall inequality. By introducing a time-discrete system, the error is separated into two parts: the temporal error and the spatial error. As the result of τ-independent of the spatial error, we obtain the L∞-norm boundedness of the fully discrete solutions without any restrictions on the grid ratio. The unconditionally optimal L2-norm error estimate is then obtained naturally. Furthermore, in order to numerically solve the system with nonsmooth solutions, we construct another Galerkin FEM with nonuniform temporal meshes, and corresponding fast algorithm by using sum-of-exponentials technique is also built. Finally, numerical results are reported to show the accuracy and efficiency of the proposed FEMs and the corresponding fast algorithms.

Nonlinear dynamic analysis of the response to the excitation forces of a symmetrical on-board rotor mounted on hydrodynamic bearings using h-p finite elements method

This work aims to study the nonlinear dynamic analysis response of the external excitation forces of an on-board rotor with a mobile non-deformable supports, mounted on hydrodynamic bearings. The modeling of the structure is achieved by the hierarchical polynomial (h-p) version of the finite element method. A theoretical study based on the Euler–Bernoulli beam model made for the establishment of the kinetic energy and the strain energy of the shaft. The Reynolds equation used to determine the hydrodynamic forces of the plain bearings. The motion equations of rotor system are determined by the Lagrange method. Linear results are obtained using the implicit time-step integration scheme of Newmark, and nonlinear results obtained with a complex resolution algorithm combining Newton–Raphson's iterative incremental procedure with the Newmark temporal integration scheme. The calculation steps for the nonlinear analysis of the onboard rotor system dynamic behavior are grouped together in an application created using the MATLAB programming language, and validated with work done previously by the classical version of the finite element method. In this article, we make some examples to study the influence of the excitations of the mobile support on the behavior of the rotor and the hydrodynamic bearings.

A particle‐continuum coupling method for multiscale simulations of viscoelastic–viscoplastic amorphous glassy polymers

AbstractIn this contribution, we present a partitioned‐domain method coupling a particle domain and a continuum domain for multiscale simulations of inelastic amorphous polymers under isothermal conditions. In the continuum domain, a viscoelastic–viscoplastic constitutive model calibrated from previous molecular dynamics (MD) simulations is employed to capture the inelastic properties of the polymer. Due to the material's rate‐dependence, a temporal coupling scheme is introduced. The influence of the time‐related parameters on the computational cost and accuracy is discussed. With appropriate parameters, multiscale simulations of glassy polystyrene under various loading conditions are implemented to showcase the method's capabilities to capture the mechanical behavior of polymers with different strain rates and with non‐affine deformations of the MD domain.

First Evaluation of Temporal and Spatial Fractionation in Proton Minibeam Radiation Therapy of Glioma-Bearing Rats.

Simple SummaryProton minibeam radiation therapy (pMBRT) is a novel therapeutic approach based on a distinct dose delivery method: the dose distributions follow a pattern with regions of peaks (high doses) and valleys (low doses). pMBRT was shown to be able to widen the therapeutic window in glioma-bearing rats. In previous studies the irradiation was performed in one single fraction. The work reported in this manuscript is the first evaluation detailing the response of glioma-bearing rats to a temporal fractionation in proton minibeam radiation therapy, delivered under a crossfire geometry. A significant increase of the median survival time was obtained when the dose was delivered over two sessions as opposed to in a single fraction. This result could facilitate the path towards pMBRT treatments.(1) Background: Proton minibeam radiation therapy (pMBRT) is a new radiotherapy technique using spatially modulated narrow proton beams. pMBRT results in a significantly reduced local tissue toxicity while maintaining or even increasing the tumor control efficacy as compared to conventional radiotherapy in small animal experiments. In all the experiments performed up to date in tumor bearing animals, the dose was delivered in one single fraction. This is the first assessment on the impact of a temporal fractionation scheme on the response of glioma-bearing animals to pMBRT. (2) Methods: glioma-bearing rats were irradiated with pMBRT using a crossfire geometry. The response of the irradiated animals in one and two fractions was compared. An additional group of animals was also treated with conventional broad beam irradiations. (3) Results: pMBRT delivered in two fractions at the biological equivalent dose corresponding to one fraction resulted in the highest median survival time, with 80% long-term survivors free of tumors. No increase in local toxicity was noted in this group with respect to the other pMBRT irradiated groups. Conventional broad beam irradiations resulted in the most severe local toxicity. (4) Conclusion: Temporal fractionation increases the therapeutic index in pMBRT and could ease the path towards clinical trials.

Urban Intersection Classification: A Comparative Analysis.

Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.

SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data.

A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data. Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal-external cross-validation scheme. The predictive quality was assessed with the C-index and calibration. Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sublocation, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal-external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively. A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.

On the “Duration” of Psychological Time in To the Lighthouse

Employing Henry Bergson’s theory of “duration” in the analysis of “psychological time” in To the Lighthouse, this article seeks to reveal the temporal scheme of the novel, in which embodied the distinctive aesthetic of “psychological time”, the motif of the essence of life, and the secret of Woolf’s creation of art. In the novel, Mrs. Ramsay found solace and hope for the future when she was immersed in her flowing of consciousness featured by heterogeneous and continuous “duration” of psychological time, while Lily Briscoe, as Woolf’s “artist alter ego,” struggled against the anguish in her duration of psychological time, transforming memory into the art of eternity. With the virtuoso representation of “stream of consciousness,” Woolf amplifies psychological-time narration within the structure of the external “clock time”, uncovering the truth of human consciousness, revealing the connection between the spiritual and the objective world, and exploring the essence of life.

Spiking Autoencoders With Temporal Coding.

Spiking neural networks with temporal coding schemes process information based on the relative timing of neuronal spikes. In supervised learning tasks, temporal coding allows learning through backpropagation with exact derivatives, and achieves accuracies on par with conventional artificial neural networks. Here we introduce spiking autoencoders with temporal coding and pulses, trained using backpropagation to store and reconstruct images with high fidelity from compact representations. We show that spiking autoencoders with a single layer are able to effectively represent and reconstruct images from the neuromorphically-encoded MNIST and FMNIST datasets. We explore the effect of different spike time target latencies, data noise levels and embedding sizes, as well as the classification performance from the embeddings. The spiking autoencoders achieve results similar to or better than conventional non-spiking autoencoders. We find that inhibition is essential in the functioning of the spiking autoencoders, particularly when the input needs to be memorised for a longer time before the expected output spike times. To reconstruct images with a high target latency, the network learns to accumulate negative evidence and to use the pulses as excitatory triggers for producing the output spikes at the required times. Our results highlight the potential of spiking autoencoders as building blocks for more complex biologically-inspired architectures. We also provide open-source code for the model.

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density.

Mathematical framework for place coding in the auditory system.

In the auditory system, tonotopy is postulated to be the substrate for a place code, where sound frequency is encoded by the location of the neurons that fire during the stimulus. Though conceptually simple, the computations that allow for the representation of intensity and complex sounds are poorly understood. Here, a mathematical framework is developed in order to define clearly the conditions that support a place code. To accommodate both frequency and intensity information, the neural network is described as a space with elements that represent individual neurons and clusters of neurons. A mapping is then constructed from acoustic space to neural space so that frequency and intensity are encoded, respectively, by the location and size of the clusters. Algebraic operations -addition and multiplication- are derived to elucidate the rules for representing, assembling, and modulating multi-frequency sound in networks. The resulting outcomes of these operations are consistent with network simulations as well as with electrophysiological and psychophysical data. The analyses show how both frequency and intensity can be encoded with a purely place code, without the need for rate or temporal coding schemes. The algebraic operations are used to describe loudness summation and suggest a mechanism for the critical band. The mathematical approach complements experimental and computational approaches and provides a foundation for interpreting data and constructing models.

COMPARISON OF SUB-GRID-SCALE MODELS BASED ON PREDICTION OF COHERENT STRUCTURES IN PLANE TURBULENT JETS

A compressible plane jet at Mach 0.9 is numerically studied by large-eddy simulation. The governing equations are discretized by fourth-order spatial and third-order temporal schemes. Five sub-grid-scale (SGS) models, namely, the standard Smagorinsky model (SM), the coherent structure kinetic model (CKM) and the selective mixed-scale model (SMSM), the localized dynamic Smagorinsky model (LDSM), and the coherent-structure Smagorinsky model (CSM), are employed for closure of the sub-grid scale (SGS) terms, respectively, and compared. Proper orthogonal decomposition (POD) method is applied to extract the leading modes of the fluctuating velocity components, i.e., \begin{document}$u'$\end{document} in the streamwise, \begin{document}$v'$\end{document} in the lateral and \begin{document}$w'$\end{document} in the spanwise. The averaged flow fields, dissipation, the instantaneous vortical structures and the coherent structures represented by the leading POD modes, are compared. The leading POD modes of \begin{document}$u'$\end{document} are two longitudinal stripes with fracted contours in the turbulent region, representing the multi-scaled flow and the decay of fluctuation strength. The leading modes of \begin{document}$v'$\end{document} are a row of ribs with the lateral size growing along the streamwise, while the modes of \begin{document}$\left( {u', v'} \right)$\end{document} are in a circular flow pattern around the ends of the ribs. The circular pattern penetrates both the jet flow and the peripheral flow, representing the flow entrainment. The leading modes of \begin{document}$w'$\end{document} are a train of ridges along streamwise. Their positive and negative values represent the spanwise stretching pattern of the coherent structures. In comparison of these five SGS models, the instantaneous multi-scale vortical flow is not effectively predicted by the SM and the CKM, as the small vortical scale is smeared. The POD modes are found to be sensitive to the sub-grid dissipation, since the valley regions of the mode of \begin{document}$u'$\end{document} coincide with the peak dissipation regions predicted by the CKM. The circular flow pattern is also not clearly predicted by the CKM. Neither the ridge pattern of the mode of \begin{document}$w'$\end{document} is predicted by the SM. On the other hand, the multi-scales turbulent flow and the flow patterns of POD modes are well predicted by the CSM, the SMSM, and the LDSM, in which the CSM is computationally more efficient.

Impact of rainfall spatiotemporal variability and model structures on flood simulation in semi-arid regions

The spatiotemporal heterogeneity in precipitation and underlying surfaces and hybrid runoff generation mechanism make hydrological modelling and forecasting in semi-arid regions becoming a challenging work. Therefore, to provide a reference for the development of hydrological models in such regions, two nested hydrological experimental watersheds in semi-arid regions were selected for attribution analysis. Based on the concept of large-sample hydrology, large-scale numerical simulation experiments were performed by constructing different spatial and temporal scale rainfall schemes and combining three hydrological models with different runoff generation mechanisms. Finally, the influences of the time step, station density, and model structure on the flood simulations in semi-arid regions were evaluated. The spatial interpolation technique was used simultaneously to describe the high-dimensional complicated nonlinear relationships between the influencing factors and simulation results. The results show the following: (1) the flood simulation accuracy was more sensitive to the time step than the spatial station density of the rainfall schemes and was highly dependent on the time step of the original observation data, and (2) compared with the accuracy of the rainfall schemes, the model structure plays a dominant role in flood simulation accuracy. Thus, the hybrid model has significant potential for flood forecasting in semi-arid regions by combining different runoff generation mechanisms. (3) The spatial interpolation technique based on the k-nearest neighbour algorithm can construct a high-dimensional distribution between the influencing factors and model simulation accuracy, and describe the complicated relationships among the time step, station density, model structure, and flood simulations.

Finite element solution of nonlocal Cahn–Hilliard equations with feedback control time step size adaptivity

AbstractIn this study, we evaluate the performance of feedback control‐based time step adaptivity schemes for the nonlocal Cahn–Hilliard equation derived from the Ohta–Kawasaki free energy functional. The temporal adaptivity scheme is recast under the linear feedback control theory equipped with an error estimation that extrapolates the solution obtained from an energy‐stable, fully implicit time marching scheme. We test three time step controllers with different properties: a simple Integral controller, a complete proportional‐integral‐derivative controller, and the PC11 predictive controller. We assess the performance of the adaptive schemes for the nonlocal Cahn–Hilliard equation in terms of the number of time steps required for the complete simulation and the computational effort measured by the required number of nonlinear and linear solver iterations. We also present numerical evidence of mass conservation and free energy decay for simulations with the three different time step controllers. The PC11 predictive controller is the best in all three‐dimensional test cases.

Optimizing bat bioacoustic surveys in human‐modified Neotropical landscapes

AbstractDuring the last decades, the use of bioacoustics as a non‐invasive and cost‐effective sampling method has greatly increased worldwide. For bats, acoustic surveys have long been known to complement traditional mist‐netting, however, appropriate protocol guidelines are still lacking for tropical regions. Establishing the minimum sampling effort needed to detect ecological changes in bat assemblages (e.g., activity, composition, and richness) is crucial in view of workload and project cost constraints, and because detecting such changes must be reliable enough to support effective conservation management. Using one of the most comprehensive tropical bat acoustic data sets, collected in the Amazon, we assessed the minimum survey effort required to accurately assess the completeness of assemblage inventories and habitat selection in fragmented forest landscapes for aerial insectivorous bats. We evaluated a combination of 20 different temporal sampling schemes, which differed regarding number of hours per night, number of nights per site, and sampling only during the wet or dry season, or both. This was assessed under two different landscape scenarios: in primary forest fragments embedded in a matrix of secondary forest and in the same forest fragments, but after they had been re‐isolated through clearing of the secondary forest. We found that the sampling effort required to achieve 90% inventory completeness varied considerably depending on the research aim and the landscape scenario evaluated, averaging ~80 and 10 nights before and after fragment re‐isolation, respectively. Recording for more than 4 h per night did not result in a substantial reduction in the required number of sampling nights. Regarding the effects of habitat selection, except for assemblage composition, bat responses in terms of richness, diversity, and activity were similar across all sampling schemes after fragment re‐isolation. However, before re‐isolation, a minimum of four to six sampling hours per night after dusk and three to five nights of sampling per site were needed to detect significant effects that could otherwise go unnoticed. Based on our results, we propose guidelines that will aid to optimize sampling protocols for bat acoustic surveys in the Neotropics.